Supply Chain Digital Twins: An Evolution, Not a Breakthrough

Digital twins help optimize drug production processes by modeling the thousands of interactions that cells, raw materials, and reagents undergo in culture. And new analysis suggests they could do the same thing for supply chains.

Researchers at the U.S. National Institute of Standards and Technology (NIST) and EMD Millipore put forward the idea, arguing that twins could make drug distribution, which is also characterized by thousands of interactions, more resilient and efficient.

Lead author Perawit Charoenwut, a logistics researcher at NIST’s systems integration division, tells GEN, “A digital twin could be extremely helpful in all phases of the biopharmaceutical supply chain. Starting from demand planning triggered by global events such as pandemics, regional disease outbreaks, aging demographics, etc., through to being able to provide visibility on capacity requirements and limitations.”

In silico models could also provide solutions to disruption by identifying alternative supply options, such as distribution centers or regional inventories, in less time, Charoenwut says.

“Digital twins could also be helpful in evaluating different suppliers by running simulations on their potential performance, based on different demand scenarios versus their individual capacities and capabilities,” he continues.

Standards

In theory, digital twins are a good option for supply chain modeling and management. In practice, however, firms interested in the approach will need to overcome some technical challenges.

For example, one major hurdle is the lack of data standardization, according to study co-author Boonserm Kulvatunyou, PhD, a computer engineer at NIST. “Supply chain digital twins require data from across organizations and third-party sources,” he tells GEN. “The lack of industry standards creates challenges in obtaining all the necessary data.”

With this in mind, the NIST’s Industrial Ontology Foundry (IOF) is working with the National Innovation Institute for Manufacturing Biopharmaceuticals (NIIMBL) to develop open-source ontology and schema standards for connecting data.

Kulvatunyou says, “The aim is to provide a semantic foundation for connecting data and knowledge across the manufacturing and supply chain operations.

“Further work is being conducted to cover broader materials, processes, and quality data,” he says. “We would like to invite industry and academia to join this effort and benefit from these new standards.”

Industry interest

Biopharma firms interested in digital supply chains will also need to establish a solid data infrastructure, according to Charoenwut, who says companies should start small and pace themselves.

“We think that biopharma companies do believe that digital twins could make a significant difference in their supply chain efficiency and resiliency. Many of them are probably building prototypes and proofs-of-concept to demonstrate the value and potential benefits, but then soon realize the digital data foundation gaps that need to be addressed in parallel in order to fully adopt this technology.

“As digital twins can vary in detail and complexity, companies should strategize digital twin adoption by starting with lower-complexity cases based on available digital data and progressively moving up the scale to gain greater precision and new capabilities. In other words, the implementation of digital twins should be viewed as an evolution rather than a breakthrough,” he says.

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